With the development of digital technologies and their increasing application in government, digital transformation is a wave rolling up the world. Previous studies had investigated some factors that affect digital transformation. But there is little research on the impact of organizational agility on digital transformation in government. To fill this gap, based on the dynamic capabilities view, this study aims to investigate how organizational agility affects digital transformation and dynamic capabilities as antecedents and factors impacting organizational agility. A survey study was conducted to empirically test the model. The data were collected from 313 government employees in government departments. The findings suggest that (1) organizational agility significantly influences digital transformation and (2) dynamic capabilities are important predictors of organizational agility.

To achieve sustainable urban development, many countries around the world are exploring the path of smart cities. The progress and development of digital technology has paved a promising road. Digital transformation of the government is an important factor for smart cities in China. Scholars are increasingly interested in the digital transformation of government, but there is little empirical research on the driving factors of digital transformation among local governments. Based on the technology–organization–environment (TOE) framework, this study explores the driving factors of digital transformation among local governments in China. A questionnaire survey and the structural equation model were used. Technological readiness, organizational efficiency, public service delivery, citizens’ expectations and superior pressure are significant driving factors. Technological readiness is the first factor affecting the government’s digital transformation. Applying the identified drivers, the results of this research could be used as orientation towards, and provide concrete practical approaches for, successful digital transformation among local governments, which would lay a foundation for smart city construction.

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM_2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM_2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM_2.5. By averaging the modeled daily PM_2.5 concentration, we produced a yearly PM_2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM_2.5 with observational data, the coefficient of determination ($R^2$) of the modeling was 0.85, the root means square error ($RMSE$) was 14.63 μg/m³, and the mean absolute error ($MAE$) was 10.03 μg/m³. The quality assessment of the synthesized yearly PM_2.5 concentration dataset shows that $R^2$ = 0.77, $RMSE$ = 6.92 μg/m³, and $MAE$ = 5.42 μg/m³. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM_2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM_2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM_2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

With the continuous improvement of living quality, food safety and healthy consumption awareness of urban residents have increased significantly. Green agricultural products marked as healthy, safe, and nutritious have become popular. At present, green agro-food is boosted by markets and policies in China, but consumers’ trust is not optimistic. Based on the theory of perceived risk and planned behavior, this research constructs a model to analyze the decision-making of urban residents’ green agricultural product consumption from the aspects of personal attitude, perceived risk, and subjective norm. Through a questionnaire survey of 329 residents in Tianjin, the empirical results show that: (1) Personal attitude and subjective norm positively affect residents’ willingness to consume. (2) Perceived risk negatively affects residents’ willingness to consume. (3) Functional risk has the highest impact, followed by time risk, financial risk, and psychological risk. In this regard, urban residents’ consumption of green agro-food could be continuously explored and promoted from the aspects of traceability supervision, circulation efficiency, and product brand and publicity.

High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R² = 0.77 for coniferous forest, R² = 0.76 for shrub-grass vegetation, R² = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

Developing countries generally face the problem of sustainable agricultural production during the process of agricultural modernization. Farmland trusteeship is an emerging mode of sustainable agricultural production and has played an important role in China. At present, the Chinese government has taken it as a pilot mode, but its effect also depends on the extensive participation of peasants. Based on the theory of planned behavior (TPB) and structural equation model (SEM) method, the paper analyzed peasants’ participation intention, behavior, drivers, and the influence of policy support on peasants’ participation behavior, using survey data of Jiangsu province and peasants’ participation variables. The results suggest that: (1) The behavioral attitude, subjective norms, and perceived behavioral control variables of peasants’ participation in farmland trusteeship have a significant direct impact on peasants’ willingness to participate, and these three factors indirectly influence peasants’ participation behavior by influencing peasants’ intention; (2) perceptual behavioral control variables such as the size of farmland trusteeship organization had no significant influence on peasants’ participation behaviors; (3) government policy support has a direct and significant impact on peasants’ participation behaviors. These findings not only generate broad direct implications for Chinese policymakers to improve peasants’ participation for sustainable agricultural production, but also provide lessons for other developing countries for agricultural modernization.